>_ building intelligent, explainable systems

Danyal Ejaz

AI / Machine Learning & Data Science Engineer

Computer Science student turning messy data into deployable, agronomically- and clinically-sound machine learning products — with a QA mindset for shipping quality software.

99.3% Crop model accuracy
100K+ Health records analyzed
2+ yrs Community leadership

Get to know more

About Me

I'm Danyal Ejaz, a final-year Computer Science student at the University of Swabi, specializing in Machine Learning, Data Science, and full-stack development. I enjoy taking a problem from raw data all the way to a deployed, explainable product that people can actually use.

My work spans AI research, hands-on model building, and software QA — from training crop and health prediction models to testing production software for defects. I care about explainability, clean pipelines, and shipping things that hold up in the real world.

Experience

AI Research, QA & Digital Media internships, plus community leadership at Cloud Native Islamabad

Education

B.S. Computer Science — University of Swabi (2022–2026)
A.S. Computer Science — University of the People
Solana Bootcamp — Encode Club (2023)

Where I've worked

Experience

Community Manager

May 2024 – Present

Cloud Native Islamabad · Islamabad, PK

  • Lead and grow the local cloud-native community, organizing events and meetups around Kubernetes and open-source tooling.
  • Coordinate speakers, partners, and volunteers to deliver engaging technical sessions.
  • Drive outreach and engagement to build an active, welcoming developer community.

QA Intern

Dec 2024 – Feb 2025

10Pearls · Remote (Islamabad, PK)

  • Conducted thorough testing to identify and report software defects, ensuring product quality.
  • Developed and executed test cases to validate system functionality.
  • Documented results and delivered detailed reports to improve performance.
  • Collaborated with developers to troubleshoot and resolve issues.

Community Cohort #4

Apr 2024 – Jun 2024

The Community Collective · Remote (Perth, AU)

  • Selected for an 8-week program where community builders, operators, and founders level up together.
  • Collaborated with global community leaders to design impactful, sustainable communities.
  • Applied frameworks for community strategy, engagement, and growth.

AI Research Intern

Nov 2023 – Jan 2024

GENIE AI · Remote (Toronto, ON, CA)

  • Collaborated with the research team to develop, implement, and refine AI algorithms.
  • Analyzed and interpreted results, providing insights and recommendations.
  • Maintained detailed documentation of findings, methodologies, and experiments.
  • Prepared and delivered presentations on progress to the research team.

Outreach Intern

Sep 2023 – Dec 2023

Firefly NGO · Remote

  • Researched potential collaboration partners in the mental health field.
  • Developed creative ideas for impactful partnerships aligned with Firefly's values.
  • Established connections with organizations to discuss collaboration opportunities.

Digital Media Intern

Jun 2023 – Aug 2023

Grey-box · Remote (Montreal, QC, CA)

  • Produced short-form content from podcast episodes to expand audience reach.
  • Created SEO-optimized posts and graphics, scheduling across platforms.
  • Worked with podcast and marketing teams to keep brand messaging consistent.
  • Contributed remotely with strong self-motivation and communication.

Browse my recent

Projects

AgroArc smart farmer support dashboard

AgroArc — Smart Farmer Support System

AI-powered agricultural advisory platform (Final Year Project) delivering data-driven crop selection and nutrient-based fertilizer recommendations for farmers in Pakistan. Random Forest crop model across 22 classes at 99.3% accuracy, plus a fertilizer model reaching 100%.

Python scikit-learn Random Forest Pandas NumPy
DiaFlux diabetes risk prediction dashboard

DiaFlux — Diabetes Risk Prediction

ML health analytics system predicting Type 2 diabetes risk from health metrics and lifestyle over a 100K+ record dataset. Compared Logistic Regression, Random Forest, SVM, and Gradient Boosting, with a real-time lifestyle-impact simulation. Containerized with Docker and deployed to Hugging Face Spaces.

Python scikit-learn Flask Streamlit Docker

My toolkit

Technical Skills

Languages

Python JavaScript Go SQL HTML/CSS Shell

Frameworks

React Node.js Flask FastAPI Streamlit WordPress

ML & Data Libraries

scikit-learn Pandas NumPy XGBoost Matplotlib Seaborn Joblib

Developer Tools

Git Docker Linux Azure Google Cloud Hugging Face Jupyter

Concepts

Machine Learning Data Analysis REST APIs Software Testing / QA CI/CD SEO Project Management Community Management

Recognition & learning

Certifications & Honors

Honors & Awards

  • Dan Kohn Scholarship — KubeCon + CloudNativeCon North America 2023

Certifications

  • Gemini Certified Educator
  • Git and GitHub
  • The Community MBA
  • Miro Essentials
  • Notion: Get Productive & Organized (ADPList)

Get in touch

Let's Build Something

Open to internships, research, and ML/data roles. The fastest way to reach me is email.